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  • 标题:Topological Characterization of Haze Episodes Using Persistent Homology
  • 本地全文:下载
  • 作者:Nur Fariha Syaqina Zulkepli ; Mohd Salmi Md Noorani ; Fatimah Abdul Razak
  • 期刊名称:Aerosol and Air Quality Research
  • 印刷版ISSN:1680-8584
  • 出版年度:2019
  • 卷号:19
  • 期号:7
  • 页码:1614-1624
  • DOI:10.4209/aaqr.2018.08.0315
  • 语种:English
  • 出版社:Chinese Association for Aerosol Research in Taiwan
  • 摘要:Haze is one of the major environmental issues that have continuously vexed countries worldwide, including Malaysia, for the last three decades. Therefore, this study aims to investigate the differences between the topological features of months with and those without haze episodes observed at air quality monitoring stations located in the areas of Jerantut, Klang, Petaling Jaya and Shah Alam. We employ persistent homology, which is a method of topological data analysis (TDA) that focuses on connected components and holes in the data, to characterize the local particulate matter (PM10). The summary statistics reveal drastic changes in the lifetimes of the topological data from every station during haze episodes, highlighting the possibility of developing an early detection system for haze based on our approach.
  • 关键词:Haze;Particulate matter;Persistent homology;Time delay embedding;Topological data analysis
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